sathish2352 commited on
Commit
a3cbe08
·
verified ·
1 Parent(s): bf95590

Update models.py

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Files changed (1) hide show
  1. models.py +12 -0
models.py CHANGED
@@ -1,20 +1,32 @@
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch
 
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  def load_model():
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  model_path = "sathish2352/email-classifier-model"
 
 
 
 
 
 
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  tokenizer = AutoTokenizer.from_pretrained(model_path)
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  model = AutoModelForSequenceClassification.from_pretrained(model_path)
 
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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  model.to(device)
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  model.eval()
 
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  return tokenizer, model, device
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  def classify_email(text, tokenizer, model, device):
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  inputs = tokenizer(text, return_tensors="pt", max_length=256, padding="max_length", truncation=True)
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  inputs = {k: v.to(device) for k, v in inputs.items()}
 
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  with torch.no_grad():
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  logits = model(**inputs).logits
 
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  label_map = {0: "Incident", 1: "Request", 2: "Change", 3: "Problem"}
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  pred = torch.argmax(logits, dim=1).item()
 
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  return label_map[pred]
 
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch
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+ import os
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  def load_model():
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  model_path = "sathish2352/email-classifier-model"
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+
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+ # Set HF_HOME to use a writable cache dir
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+ os.environ["HF_HOME"] = "/tmp/huggingface"
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+ os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface/transformers"
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+ os.makedirs("/tmp/huggingface/transformers", exist_ok=True)
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+
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  tokenizer = AutoTokenizer.from_pretrained(model_path)
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  model = AutoModelForSequenceClassification.from_pretrained(model_path)
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+
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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  model.to(device)
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  model.eval()
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+
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  return tokenizer, model, device
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  def classify_email(text, tokenizer, model, device):
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  inputs = tokenizer(text, return_tensors="pt", max_length=256, padding="max_length", truncation=True)
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  inputs = {k: v.to(device) for k, v in inputs.items()}
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+
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  with torch.no_grad():
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  logits = model(**inputs).logits
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+
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  label_map = {0: "Incident", 1: "Request", 2: "Change", 3: "Problem"}
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  pred = torch.argmax(logits, dim=1).item()
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+
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  return label_map[pred]